EGPDI: identifying protein-DNA binding sites based on multi-view graph embedding fusion.

Mechanisms of protein-DNA interactions are involved in a wide range of biological activities and processes. Accurately identifying binding sites between proteins and DNA is crucial for analyzing genetic material, exploring protein functions, and designing novel drugs. In recent years, several computational methods have been proposed as alternatives to time-consuming and expensive traditional experiments. However, accurately predicting protein-DNA binding sites still remains a challenge. Existing computational methods often rely on handcrafted features and a single-model architecture, leaving room for improvement. We propose a novel computational method, called EGPDI, based on multi-view graph embedding fusion. This approach involves the integration of Equivariant Graph Neural Networks (EGNN) and Graph Convolutional Networks II (GCNII), independently configured to profoundly mine the global and local node embedding representations. An advanced gated multi-head attention mechanism is subsequently employed to capture the attention weights of the dual embedding representations, thereby facilitating the integration of node features. Besides, extra node features from protein language models are introduced to provide more structural information. To our knowledge, this is the first time that multi-view graph embedding fusion has been applied to the task of protein-DNA binding site prediction. The results of five-fold cross-validation and independent testing demonstrate that EGPDI outperforms state-of-the-art methods. Further comparative experiments and case studies also verify the superiority and generalization ability of EGPDI.

Double-Enhanced Photothermal Lateral Flow Biosensor Based on Dual Gold Nanoparticle Conjugates.

Bacterial toxins emerge as the primary triggers of foodborne illnesses, posing a significant threat to human health. To ensure food safety, it is imperative to implement point-of-care testing methods. Lateral flow biosensors (LFBs) based on gold nanoparticles (GNPs) have been commonly used for rapid detection, but their applicationis limited by low sensitivity. Based on the localized surface plasmon resonance and photothermal effect of dual gold nanoparticle conjugates (DGNPs), we developed a smartphone-integrated photothermal LFB (PLFB) with double-enhanced colorimetric and photothermal sensitivity. Through numerical simulations, we verified that DGNPs have significantly enhanced photothermal performance compared to single 15 nm GNPs (SGNPs), and applied DGNPs in PLFB for the detection of staphylococcus enterotoxin A (SEA). The colorimetric and photothermal limits of detection of DGNPs-based PLFB for SEA were 0.091 and 0.0038 ng mL-1, respectively. Compared with the colorimetric detection of the SGNPs-based LFB, the colorimetric detection sensitivity of the DGNPs-based PLFB was increased by 10.7 times, and the photothermal detection sensitivity was further improved by 255.3 times. Moreover, the PLFB exhibits robust reproducibility and exceptional specificity and is applicable for detecting SEA in milk samples. This smartphone-integrated PLFB based on DGNPs allows users to detect toxins simply, conveniently, and quickly and has huge application potential in the field of food safety.

Artificial intelligence-accelerated high-throughput screening of antibiotic combinations on a microfluidic combinatorial droplet system.

Microfluidic platforms have been employed as an effective tool for drug screening and exhibit the advantages of lower reagent consumption, higher throughput and a higher degree of automation. Despite the great advancement, it remains challenging to screen complex antibiotic combinations in a simple, high-throughput and systematic manner. Meanwhile, the large amounts of datasets generated during the screening process generally outpace the abilities of the conventional manual or semi-automatic data analysis. To address these issues, we propose an artificial intelligence-accelerated high-throughput combinatorial drug evaluation system (AI-HTCDES), which not only allows high-throughput production of antibiotic combinations with varying concentrations, but can also automatically analyze the dynamic growth of bacteria under the action of different antibiotic combinations. Based on this system, several antibiotic combinations displaying an additive effect are discovered, and the dosage regimens of each component in the combinations are determined. This strategy not only provides useful guidance in the clinical use of antibiotic combination therapy and personalized medicine, but also offers a promising tool for the combinatorial screenings of other medicines.

Rapid Fabrication of Sterile Medical Nasopharyngeal Swabs by Stereolithography for Widespread Testing in a Pandemic.

The 3D printing of nasopharyngeal swabs during the COVID-19 pandemic presents a central case of how to efficiently address a break in the global supply chain of medical equipment. Herein a comprehensive study of swab design considerations for mass production by stereolithography is presented. The retention and comfort performance of a range of novel designs of 3D-printed swabs are compared with the standard flocked-head swab used in clinical environments. Sample retention of the 3D swab is governed by the volume, porosity density, and void fraction of the head as well as by the pore geometry. 3D-printed swabs outperform conventional flock-head swabs in terms of sample retention. It is argued that mechanically functional designs of the swab head, such as corkscrew-shaped heads and negative Poisson ratio heads, maximize sample retention and improve patient comfort. In addition, available designs of swab shafts for an optimized sample collection procedure are characterized. The study is conducted in vitro, using artificial mucus, covering the full range of human mucus viscosities in a 3D-printed model of a nasal cavity. The work sets the path for the resilient supply of widespread sterile testing equipment as a rapid response to the current and future pandemics.

Towards Digital Manufacturing of Smart Multimaterial Fibers.

Fibers are ubiquitous and usually passive. Optoelectronics realized in a fiber could revolutionize multiple application areas, including biosynthetic and wearable electronics, environmental sensing, and energy harvesting. However, the realization of high-performance electronics in a fiber remains a demanding challenge due to the elusiveness of a material processing strategy that would allow the wrapping of devices made in crystalline semiconductors, such as silicon, into a fiber in an ordered, addressable, and scalable manner. Current fiber-sensor fabrication approaches either are non-scalable or limit the choice of semiconductors to the amorphous ones, such as chalcogenide glasses, inferior to silicon in their electronic performance, resulting in limited bandwidth and sensitivity of such sensors when compared to a standard silicon photodiode. Our group substantiates a universal in-fiber manufacturing of logic circuits and sensory systems analogous to very large-scale integration (VLSI), which enabled the emergence of the modern microprocessor. We develop a versatile hybrid-fabrication methodology that assembles in-fiber material architectures typical to integrated microelectronic devices and systems in silica, silicon, and high-temperature metals. This methodology, dubbed "VLSI for Fibers," or "VLSI-Fi," combines 3D printing of preforms, a thermal draw of fibers, and post-draw assembly of fiber-embedded integrated devices by means of material-selective spatially coherent capillary breakup of the fiber cores. We believe that this method will deliver a new class of durable, low cost, pervasive fiber devices, and sensors, enabling integration of fabrics met with human-made objects, such as furniture and apparel, into the Internet of Things (IoT). Furthermore, it will boost innovation in 3D printing, extending the digital manufacturing approach into the nanoelectronics realm.

UV-activated persulfate oxidation and regeneration of NOM-Saturated granular activated carbon.

A new method of ultraviolet light (UV) activated persulfate (PS) oxidation was investigated to regenerate granular activated carbon (GAC) in drinking water applications. The improvements in iodine and methylene blue numbers measured in the GAC after ultraviolet- (UV) activated persulfate suggested that the GAC preloaded with natural organic matter (NOM) was chemically regenerated. An experimental matrix for UV-activated persulfate regeneration included a range of persulfate doses and different UV wavelengths. Over 87% of the initial iodine number for GAC was restored under the optimum conditions, perfulfate dosage 60 g/L and UV exposure 1.75 × 10(4) mJ/cm(2). The persulfate dosages had little effect on the recovery of the methylene blue number, which was approximately 65%. Persulfate activation at 185 nm was superior to activation at 254 nm. UV activation of persulfate in the presence of GAC produced acid, lowering the solution pH. Higher persulfate concentrations and UV exposure resulted in greater GAC regeneration. Typical organic and inorganic byproducts (e.g., benzene compounds and sulfate ions) were measured as a component of treated water quality safety. This study provides a proof-of-concept that can be used to optimize pilot-scale and full-scale UV-activated persulfate for regeneration of NOM-saturated GAC.